Primary version
Please provide a valid training processor option
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, InputLayer, Dropout, Conv1D, Conv2D, Flatten, Reshape, MaxPooling1D, MaxPooling2D, BatchNormalization
from tensorflow.keras.optimizers import Adam
# model architecture
model = Sequential()
model.add(Reshape((int(input_length / 1), 1), input_shape=(input_length, )))
model.add(Conv1D(16, kernel_size=4, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=4, strides=1, padding='same'))
model.add(Dropout(0.1))
model.add(Conv1D(32, kernel_size=4, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=4, strides=1, padding='same'))
model.add(Dropout(0.1))
model.add(Flatten())
model.add(Dense(classes, activation='softmax', name='y_pred'))
# this controls the learning rate
opt = Adam(lr=0.0005, beta_1=0.9, beta_2=0.999)
# this controls the batch size, or you can manipulate the tf.data.Dataset objects yourself
BATCH_SIZE = 32
train_dataset, validation_dataset = set_batch_size(BATCH_SIZE, train_dataset, validation_dataset)
callbacks.append(BatchLoggerCallback(BATCH_SIZE, train_sample_count))
# train the neural network
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
model.fit(train_dataset, epochs=500, validation_data=validation_dataset, verbose=2, callbacks=callbacks)
Input layer (31 features)
Reshape layer (1 columns)
1D conv / pool layer (32 neurons, 16 kernel size, 1 layer)
Dropout (rate 0.1)
1D conv / pool layer (16 neurons, 4 kernel size, 1 layer)
Dropout (rate 0.1)
Flatten layer
Output layer (3 classes)
Model version: